CN109348501B - Indoor and outdoor distinguishing method based on LTE (Long term evolution) signals - Google Patents

Indoor and outdoor distinguishing method based on LTE (Long term evolution) signals Download PDF

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CN109348501B
CN109348501B CN201811482435.9A CN201811482435A CN109348501B CN 109348501 B CN109348501 B CN 109348501B CN 201811482435 A CN201811482435 A CN 201811482435A CN 109348501 B CN109348501 B CN 109348501B
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indoor
outdoor
rsrp
lte
distinguishing
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CN109348501A (en
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马琳
黄鹏飞
徐玉滨
孙永亮
张永亮
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Harbin Institute of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

Abstract

The invention provides an indoor and outdoor distinguishing method based on LTE signals, and belongs to the technical field of signal processing. Firstly, discretely selecting a plurality of reference points indoors and outdoors, respectively carrying out LTE signal acquisition at the reference points, and recording an LTE signal every second within acquisition time; then, each reference point is endowed with a label, and the label comprises the geographic position of the reference point and indoor and outdoor state information; acquiring RSRP values and ECI values of a main service cell and an adjacent cell in an LTE signal, and completing the establishment of a position fingerprint Map Radio Map by combining indoor and outdoor states of the signal in a label; then training the Radio Map by using a support vector machine algorithm to obtain an indoor and outdoor distinguishing model; and finally, taking the RSRP vector to be distinguished as the input of the indoor and outdoor distinguishing model, wherein the output of the model is the indoor and outdoor distinguishing result. The invention solves the problem of low indoor and outdoor distinguishing precision of the existing positioning technology. The invention can be used for indoor and outdoor distinguishing and positioning.

Description

Indoor and outdoor distinguishing method based on LTE (Long term evolution) signals
Technical Field
The invention relates to an indoor and outdoor distinguishing method based on LTE signals, and belongs to the technical field of signal processing.
Background
With the rapid development of communication technology, smart phones are increasingly popular, and people have higher requirements for services provided by smart phones. Among them, Location Based Service (LBS) has become a basic Service requirement necessary for people's daily work and life. The LBS provides a location information-based service to a terminal through the support of a geographic information system platform. As a most widely used positioning means, a Global Navigation Satellite System (GNSS) can achieve high positioning accuracy, but is sensitive to influences such as obstruction, weather changes, and the like, and this disadvantage is particularly prominent in urban areas. Urban areas have dense buildings and larger indoor positioning requirements, and the development space of GNSS positioning is compressed. On the other hand, obtaining the positioning result of the user's GNSS requires permission of the user terminal, and it is difficult for manufacturers such as operators who provide basic services to obtain this data, so that GNSS positioning has certain limitations. In contrast, positioning techniques based on LTE signals are less affected by building shadowing, weather changes, usage rights, and the like. The perfection of cellular mobile communication networks in urban areas enables the LTE signal resources to be rich, the number of base stations to be large and the mutual distances to be short. Meanwhile, the device can provide higher positioning precision and is widely valued by researchers.
The existing research focuses on an indoor positioning technology or an outdoor positioning technology, and an indoor and outdoor distinguishing algorithm is rarely researched in a focused manner. The indoor positioning is mainly applied to navigation service in large buildings, and the commonly used technologies include Wi-Fi technology, Bluetooth technology, ZigBee technology, infrared technology, visual positioning technology and the like. Outdoor positioning can be used for positioning, navigation, monitoring, drawing and the like, and common positioning technologies include a GNSS technology, an Ultra Wideband (UWB) technology, an LTE technology and the like. Positioning technologies based on LTE signals are mainly classified into two categories: ranging-based positioning techniques and location fingerprint-based positioning techniques. However, in a region with dense buildings, the antenna hanging height of the base station is relatively low, and the influence of non-line-of-sight on the propagation of the LTE signal is serious, which restricts the practical application of the positioning technology based on ranging. On indoor and outdoor areas, the positioning technology based on the position fingerprints has better operability and more improvement and expansion directions.
Most of indoor and outdoor distinguishing algorithms are roughly distinguished and serve fine indoor positioning (or outdoor positioning), namely indoor and outdoor distinguishing is not the final purpose, so that the precision requirement is not high, only LTE signal data is primarily processed, and therefore the precision of indoor positioning (or outdoor positioning) is improved, and the high-precision indoor and outdoor distinguishing algorithms are not independently researched.
Disclosure of Invention
The invention provides an indoor and outdoor distinguishing method based on LTE signals, which aims to solve the problem that the indoor and outdoor distinguishing precision of the existing positioning technology is not high.
The indoor and outdoor distinguishing method based on the LTE signal is realized by the following technical scheme:
the method comprises the following steps that firstly, under the scene that indoor and outdoor distinguishing is needed, a plurality of reference points are respectively selected in a scattered mode indoors and outdoors, LTE signal acquisition is respectively carried out on the reference points through a platform carrying LTE signal acquisition equipment, and an LTE signal is recorded every second within the acquisition duration;
step two, endowing each reference point with a label, wherein the label comprises the geographical position of the reference point and indoor and outdoor state information;
acquiring Reference Signal Received Power (RSRP) values of a main service cell and an adjacent cell in the acquired LTE signals, and evolved universal terrestrial Radio access network (ECI) cell identifications of the main service cell and the adjacent cell, and completing the establishment of a position fingerprint Map Radio Map (Map) by combining indoor and outdoor states of signals in a label;
dividing the Radio Map into an indoor category and an outdoor category, and training the Radio Map by using a support vector machine algorithm to obtain an indoor and outdoor distinguishing model;
acquiring RSRP values of a main serving cell and an adjacent cell in the signal to be distinguished and ECIs of the main serving cell and the adjacent cell to form RSRP vectors to be distinguished; and taking the RSRP vector to be distinguished as the input of the indoor and outdoor distinguishing model, wherein the output of the model is the indoor and outdoor distinguishing result.
As a further elaboration of the above technical solution:
further, the establishing of the Radio Map in the third step specifically includes the following steps:
step three, calculating the set of all ECIs in the collected LTE signals; let the set of all cells in the ith LTE signal be { ECI }iThe total number of signals is m, i is 1,2, …, m; set of all ECIsallComprises the following steps:
Figure BDA0001893708260000021
wherein N is the total number of ECIs; ECIjIs ECIallThe jth ECI in (1); j ═ 1,2, …, N;
step three, the RSRP value of the jth cell in the ith LTE signal
Figure BDA0001893708260000022
The following processes were carried out
Figure BDA0001893708260000023
Wherein RSRP is a fixed value M less than the RSRP value in all the collected LTE signals;
step three, obtaining the RSRP matrix through the processing process of the step three:
Figure BDA0001893708260000024
step three and four, constructing LTE data label vector
L＝[l1l2… lm]T(4)
Wherein, superscript T represents transposition; liA label of a reference point where the ith LTE signal is located; if the reference point is located indoors, theni1, otherwisei＝+1
Step three, obtaining an offline database Radio Map:
D＝[L P](5)。
further, the step four of training the Radio Map by using the support vector machine algorithm to obtain the indoor and outdoor partial model includes the specific steps of:
step four, determining an objective function:
Figure BDA0001893708260000031
wherein the content of the first and second substances,
Figure BDA0001893708260000032
as a transpose of the ith row of the RSRP matrix P in the location fingerprint Map Radio Map,
Figure BDA0001893708260000033
and
Figure BDA0001893708260000034
is a variable to be optimized;
Figure BDA0001893708260000035
which represents a real number of the digital signal,
Figure BDA0001893708260000036
a real number matrix representing N rows and 1 columns;
step two, converting the formula (6) into a dual problem by using a Lagrange multiplier method:
Figure BDA0001893708260000037
wherein α ═ α1,α2,…,αm]TFor new variables to be optimized, Lagrange multiplier αi≥0；k＝1,2,…,m；
Step four and step three, solving the formula (7) to obtain a solution α ═ α of the variable to be optimized1,α2,…,αm]TSo as to obtain the optimal solution of w and b;
Figure BDA0001893708260000038
Figure BDA0001893708260000039
fourthly, determining an indoor and outdoor partial model:
Figure BDA00018937082600000310
wherein x is [ x ]ix2… xN]TIs an RSRP vector.
Further, the step five specifically comprises the following steps:
fifthly, the RSRP value of the jth cell in the signals to be distinguished
Figure BDA0001893708260000041
The following treatments were carried out:
Figure BDA0001893708260000042
obtaining RSRP vectors to be distinguished:
Figure BDA0001893708260000043
wherein the content of the first and second substances,
Figure BDA0001893708260000044
is in the signal to be distinguished
Figure BDA0001893708260000045
A corresponding RSRP value;
step five and step two, x istInput f (x), if f(s)t)>0, indicating that the distinguishing result is outdoor; if f(s)t)<0, indicating that the discrimination result is indoor; if f(s)t) And 0, indicating that the indoor and outdoor discrimination fails.
Furthermore, M in the third step is 10 dB-100 dB.
Further, in the step one, the collection time is 1.5-2.5 min.
The most prominent characteristics and remarkable beneficial effects of the invention are as follows:
the invention relates to an indoor and outdoor distinguishing method based on LTE signals, which is characterized in that on the basis of the existing hardware resources (such as a base station and the like), an indoor and outdoor distinguishing model can be established by utilizing a smart phone to easily acquire LTE signal samples, and then indoor and outdoor distinguishing results can be obtained only by taking RSRP vectors of signals to be distinguished as the input of the indoor and outdoor distinguishing model. The method can be regarded as an improvement of the traditional position fingerprint positioning method, and adopts a machine learning optimization algorithm on the basis of the traditional position fingerprint positioning. But simulation results show that compared with the position fingerprint positioning method, the method provided by the invention is obviously improved. The problem of indoor and outdoor distinguishing can be well solved based on LTE signals, and the distinguishing precision can reach more than 92%.
Drawings
FIG. 1 is a schematic view of a label of the present invention;
FIG. 2 is a schematic diagram of the composition of Radio Map data according to the present invention;
FIG. 3 is a signal composition diagram of a single LTE signal in the present invention;
FIG. 4 is a schematic flow chart of the method of the present invention; AP (Access Point)1,…,APNRepresenting N wireless access points.
Detailed Description
The first embodiment is as follows: the present embodiment is described with reference to fig. 1, fig. 2, fig. 3, and fig. 4, and the method for indoor and outdoor discrimination based on LTE signals according to the present embodiment specifically includes the following steps:
the method comprises the steps that firstly, under the scene that indoor and outdoor distinguishing is needed, a plurality of reference points are respectively selected in a scattered mode indoors and outdoors, LTE signals are respectively collected at the reference points through a platform carrying LTE signal acquisition equipment (such as a mobile phone), and one LTE signal is recorded every second within collection duration;
step two, endowing each reference point with a LABEL LABEL, wherein the LABEL comprises the geographical position of the reference point and indoor and outdoor state information; in the embodiment, the tag end bit is used for representing indoor and outdoor state information; for the two tags in fig. 1, the last bit of the tag is 0 indicating indoor, and 1 indicating outdoor.
Acquiring Reference Signal Received Power (RSRP) values of a main service cell and an adjacent cell in the acquired LTE signals, and evolved universal terrestrial Radio access network (ECI) cell identifications of the main service cell and the adjacent cell, and completing the establishment of a position fingerprint Map Radio Map (Map) by combining indoor and outdoor states of signals in a label;
step four, the step is an off-line stage. Dividing the Radio Map into an indoor category and an outdoor category, and training the Radio Map by utilizing a Support Vector Machine (SVM) algorithm to obtain an indoor and outdoor distinguishing model;
and step five, the step is an online stage. Acquiring RSRP values of a main serving cell and an adjacent cell in a signal to be distinguished and ECIs of the main serving cell and the adjacent cell to form an RSRP vector to be distinguished; and taking the RSRP vector to be distinguished as the input of the indoor and outdoor distinguishing model, wherein the output of the model is the indoor and outdoor distinguishing result.
The second embodiment is as follows: the difference between this embodiment and the specific embodiment is that the establishing of the RadioMap in step three specifically includes the following steps:
step three, calculating the set of all ECIs in the collected LTE signals; let the set of all cells in the ith LTE signal be { ECI }iThe total number of signals is m, i is 1,2, …, m; set of all ECIsallComprises the following steps:
Figure BDA0001893708260000051
where N is the total number of ECIs (not repeated); ECIjIs ECIallThe jth ECI in (1); j ═ 1,2, …, N;
step three, the RSRP value of the jth cell in the ith LTE signal
Figure BDA0001893708260000052
The following processes were carried out
Figure BDA0001893708260000053
Wherein RSRP is a fixed value M less than the RSRP value in all the collected LTE signals;
step three, obtaining the RSRP matrix through the processing process of the step three:
Figure BDA0001893708260000054
step three and four, constructing LTE data label vector
L＝[l1l2… lm]T(4)
Wherein, superscript T represents transposition; liA label of a reference point where the ith LTE signal is located; since the labels of the same reference points are the same,/iIs a discrete value; if the reference point is located indoors, theni1, otherwisei＝+1
Step three, obtaining an offline database Radio Map, as shown in fig. 2:
D＝[L P](5)
other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the difference between this embodiment and the second embodiment is that, in the fourth step, the step of training the Radio Map by using the support vector machine algorithm to obtain the indoor and outdoor partial model includes the specific steps of:
step four, determining an objective function:
Figure BDA0001893708260000061
wherein the content of the first and second substances,
Figure BDA0001893708260000062
as a transpose of the ith row of the RSRP matrix P in the location fingerprint Map Radio Map,
Figure BDA0001893708260000063
and
Figure BDA0001893708260000064
is a variable to be optimized;
Figure BDA0001893708260000065
which represents a real number of the digital signal,
Figure BDA0001893708260000066
a real number matrix representing N rows and 1 columns;
step four, the formula (6) is a typical convex quadratic optimization problem, and the formula (6) is converted into a dual problem by using a Lagrange multiplier method:
Figure BDA0001893708260000067
wherein α ═ α1,α2,…,αm]TFor new variables to be optimized, Lagrange multiplier αi≥0；k＝1,2,…,m；
Step four and step three, solving the formula (7) to obtain a solution α ═ α of the variable to be optimized1,α2,…,αm]TSo as to obtain the optimal solution of w and b;
Figure BDA0001893708260000068
Figure BDA0001893708260000069
fourthly, determining an indoor and outdoor partial model:
Figure BDA00018937082600000610
wherein x is [ x ]ix2… xN]TIs an RSRP vector.
Other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the difference between this embodiment and the third embodiment is that step five is an online positioning stage, in which the RSRP vector of the signal to be distinguished is used as the input of the indoor and outdoor distinguishing model, and the output of the model is the indoor and outdoor distinguishing result, and specifically includes the following steps:
fifthly, the RSRP value of the jth cell in the signals to be distinguished
Figure BDA0001893708260000071
The following treatments were carried out:
Figure BDA0001893708260000072
thereby obtaining the RSRP vector to be distinguished:
Figure BDA0001893708260000073
wherein the content of the first and second substances,
Figure BDA0001893708260000074
is in the signal to be distinguished
Figure BDA0001893708260000075
A corresponding RSRP value;
step two, for the indoor and outdoor distinguishing model f (x) determined in the specific step four, x is determinedtInput f (x), if f(s)t)>0, indicating that the distinguishing result is outdoor; if f(s)t)<0, indicating that the discrimination result is indoor; if f(s)t) And 0, indicating that the indoor and outdoor discrimination fails.
Other steps and parameters are the same as those in the first, second or third embodiment.
The fifth concrete implementation mode: the difference between this embodiment and the second embodiment is that M in the third embodiment is 10dB to 100 dB.
Other steps and parameters are the same as those in the first, second, third or fourth embodiments.
The sixth specific implementation mode: the difference between the first embodiment and the fifth embodiment is that the time period for collecting in the first step is 1.5-2.5 min. This acquisition time period ensures that enough signal data is acquired but not so much signal is acquired that it is time consuming and creates a large amount of data redundancy.
Other steps and parameters are the same as those in the first to fifth embodiments.
The present invention is capable of other embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and scope of the present invention.

Claims (3)

1. The indoor and outdoor distinguishing method based on the LTE signal is characterized by comprising the following steps of:
the method comprises the following steps that firstly, under the scene that indoor and outdoor distinguishing is needed, a plurality of reference points are respectively selected in a scattered mode indoors and outdoors, LTE signal acquisition is respectively carried out on the reference points through a platform carrying LTE signal acquisition equipment, and an LTE signal is recorded every second within the acquisition duration;
the collection time is 1.5-2.5 min;
step two, endowing each reference point with a label, wherein the label comprises the geographical position of the reference point and indoor and outdoor state information;
acquiring Reference Signal Received Power (RSRP) values of a main service cell and an adjacent cell in the acquired LTE signals, and evolved universal terrestrial Radio access network (ECI) cell identifications of the main service cell and the adjacent cell, and completing the establishment of a position fingerprint Map Radio Map (Map) by combining indoor and outdoor states of signals in a label; the specific process is as follows:
step three, calculating the set of all ECIs in the collected LTE signals; let the set of all cells in the ith LTE signal be { ECI }iThe total number of signals is m, i is 1,2,.. multidot.m; set of all ECIsallComprises the following steps:
Figure FDA0002595316960000011
wherein N is the total number of ECIs; ECIjIs ECIallThe jth ECI in (1); j ═ 1,2, …, N;
step three, the RSRP value of the jth cell in the ith LTE signal
Figure FDA0002595316960000012
The following processes were carried out
Figure FDA0002595316960000013
Wherein RSRP is a fixed value M smaller than the RSRP value in all the collected LTE signals, and M is 10 dB-100 dB;
step three, obtaining the RSRP matrix through the processing process of the step three:
Figure FDA0002595316960000014
step three and four, constructing LTE data label vector
L＝[l1l2…lm]T(4)
Wherein, superscript T represents transposition; liA label of a reference point where the ith LTE signal is located; if the reference point is located indoors, theni1, otherwisei＝+1
Step three, obtaining an offline database Radio Map:
D＝[L P](5)；
dividing the Radio Map into an indoor category and an outdoor category, and training the Radio Map by using a support vector machine algorithm to obtain an indoor and outdoor distinguishing model;
acquiring RSRP values of a main serving cell and an adjacent cell in the signal to be distinguished and ECIs of the main serving cell and the adjacent cell to form RSRP vectors to be distinguished; and taking the RSRP vector to be distinguished as the input of the indoor and outdoor distinguishing model, wherein the output of the model is the indoor and outdoor distinguishing result.
2. The method for indoor and outdoor discrimination based on LTE signals according to claim 1, wherein the step four of training the Radio Map by using the support vector machine algorithm to obtain the indoor and outdoor partial model comprises the following specific steps:
step four, determining an objective function:
Figure FDA0002595316960000021
wherein the content of the first and second substances,
Figure FDA0002595316960000022
as a transpose of the ith row of the RSRP matrix P in the location fingerprint Map Radio Map,
Figure FDA0002595316960000023
and
Figure FDA0002595316960000024
is a variable to be optimized;
Figure FDA0002595316960000025
which represents a real number of the digital signal,
Figure FDA0002595316960000026
a real number matrix representing N rows and 1 columns;
step two, converting the formula (6) into a dual problem by using a Lagrange multiplier method:
Figure FDA0002595316960000027
wherein α ═ α1,α2,…,αm]TFor new variables to be optimized, Lagrange multiplier αi≥0；k＝1,2,…,m；
Step four and step three, solving the formula (7) to obtain a solution α ═ α of the variable to be optimized1,α2,…,αm]TSo as to obtain the optimal solution of w and b;
Figure FDA0002595316960000028
Figure FDA0002595316960000029
fourthly, determining an indoor and outdoor partial model:
Figure FDA00025953169600000210
wherein x is [ x ]ix2…xN]TIs an RSRP vector.
3. The method for indoor and outdoor discrimination based on the LTE signal as claimed in claim 1, wherein step five specifically comprises the following steps:
fifthly, the RSRP value of the jth cell in the signals to be distinguished
Figure FDA0002595316960000031
The following treatments were carried out:
Figure FDA0002595316960000032
obtaining RSRP vectors to be distinguished:
Figure FDA0002595316960000033
wherein the content of the first and second substances,
Figure FDA0002595316960000034
is ECI in a signal to be distinguishedjA corresponding RSRP value;
step five and step two, x istInput f (x), if f(s)t)>0, indicating that the distinguishing result is outdoor; if f(s)t)<0, indicating that the discrimination result is indoor; if f(s)t) And 0, indicating that the indoor and outdoor discrimination fails.
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